Tomographic imaging is a signal acquisition and processing technology that allows for high-resolution cross-sectional imaging in biological systems. Tomographic imaging systems include, for example, optical coherence tomography systems, ultrasound imaging systems, and computed tomography. Tomographic imaging is particularly well-suited for imaging the subsurface of a vessel or lumen within the body, such as a blood vessel, using probes disposed within a catheter through a minimally invasive procedure.
Typical tomographic imaging catheters consist of an imaging core that rotates and moves longitudinally through a blood vessel, while recording an image video loop of the vessel. The motion results in a 3D dataset, where each frame provides a 360 degree slice of the vessel at different longitudinal section. Each frame, or image, consists of a set of A-lines, which are depth profiles produced by the reflected energy as a function of time.
These frames provide cardiologists with invaluable information such as the location and severity of the stenosis in a patient, the presence of vulnerable plagues, mal-apposed stents, and changes in the disease over time. The information also assists in determining the appropriate treatment plan for the patient, such as drug therapy, stent placement, angioplasty, or bypass surgery. Because a physician is relying on the quality of the image for diagnosis and course of treatment, image quality is critical.
A drawback of tomographic imaging and other signal acquisition imaging technologies is the presence of noise that disrupts the signal and reduces image quality. Noise cause by attenuating objects, such as metal stents, degrades image quality and results in high amplitude signals that appear as an actual streak within the obtained images. The resulting streak is often called a streaking artifact. In addition, periodic noise, such as noise caused by changes in polarization, can also degrade image quality. The reduced image quality caused by both streaking artifacts and periodic noise impedes a physician's ability to accurately interpret the medical image.
Typically, noise is reduced in medical images using filtering and averaging techniques. Filtering techniques include applying digital filters, such as mean and median, wavelet, anisotropic and bi-lateral filters, uniformly across A-scans. Typical averaging techniques treat noise as a uniform background disruption across an imaging data set and average the noise across A-scans. Although filters and uniform signal averaging successfully remove some noise, such techniques are inefficient at removing streaking artifacts and periodic noise.
Current techniques aimed at removing streaking artifacts are complicated and inefficient due to multiple complicated processing techniques. One technique to remove streaking artifacts utilizes nearest-neighbor pattern recognition. Using nearest neighbor pattern recognition, A-scans that intersect an attenuating object responsible for the streaking are detected. After the A-scans are detected, the A-scans are removed from the data set and the streaking artifact is replaced with interpolated data. Another technique requires obtaining repeated B-scans of a target location, registering and aligning the obtained B-scans, and averaging the A-scans across the B-scans using a weighted average. This technique is only successful in removing streaking artifacts if the attenuating object does not produce a streaking artifact uniformly across the B-scans.